Determinant of Housing Rents in Urban Albania: An Empirical Hedonic Price Application With Nsa Survey Data

Kahveci, Mustafa; Sabaj, Ernil

doi:10.15604/ejef.2017.05.02.004

Cited by 3 publications

(3 citation statements)

References 17 publications

(18 reference statements)

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“…This paper, like several studies in a large theoretical body of hedonic literature on residential property market (Baranzini and Schaerer, 2011; Walsh et al , 2015; Yamagata et al , 2016; Kahveci and Sabaj, 2017; Bedell, 2018; Beltrán et al , 2018; Du et al , 2018), is deeply rooted in Rosen’s (1974) work which provided a framework for hedonic analysis using a model of consumer bid and producer offer functions for determining the implicit price of the characteristics of a property for different consumers. The relationship between the price of housing units and housing attributes has been widely addressed in the coastal housing economics and property value modelling literature by the hedonic price models.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling coastal externalities effects on residential housing values

Ayoola

Oladapo

Ojo

et al. 2022

IJHMA

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Purpose This paper aims to examine the impact of coastline on the rental value of residential property in proximity to the coastline, using the hedonic pricing model from two perspectives. First, Model 1A–C accounted for estimating the influence of coastal amenities while controlling for other housing attributes influencing rent. Second, Model 2A–C accounted for the interaction between coastal amenities/disamenities and other housing attributes influencing rent. Design/methodology/approach A survey approach was adopted for the data collection process. For both models, property values were measured in proximity to coastline using 0–250 m, 251–500 m and 0–500 m. Findings Findings revealed that property rental value increases as we move away from the coastline when disamenities are not controlled. The results suggested that for a mean-priced home (N2,941,029 or $8,170) at the mean distance from the coastline (301.83 m), a 1% increase in distance from the coastline would result in a 0.001% or N9.77 ($0.03) increase in rental value. Practical implications The implication to real estate valuers is that varying premiums should be considered when valuing a property depending on the distance to the coastline while considering other housing attributes. Originality/value This research introduces a novel approach to the hedonic model for determining property values in proximity to coastal environment by estimating the influence of coastal amenities while controlling for other housing attributes influencing rent, on the one hand, and accounting for the interaction between coastal amenities/disamenities and other housing attributes influencing rent, on the other.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling coastal externalities effects on residential housing values

Ayoola

Oladapo

Ojo

et al. 2022

IJHMA

View full text Add to dashboard Cite

show abstract

“…Generally, it has been found that housing prices tend to be higher as the area increases [25][26][27][28][29][30], and similarly, higher floors are associated with higher housing prices [3,[31][32][33][34][35]. Additionally, factors such as the number of rooms and bathrooms are frequently taken into account, and overall, it has been observed that housing prices tend to be higher with a greater number of rooms and bathrooms [36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

“…Access to subway stations, in particular, is found to increase housing prices as accessibility improves [27,28,36,52,55,56]. Moreover, some studies [18,34,38,57] argue that proximity to bus stops leads to higher housing prices. However, there is also the consistent argument that housing prices decrease in closer proximity to bus stops [30,32].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Correlation between Air Pollution and Housing Prices in Seoul, South Korea: Application of Explainable Artificial Intelligence in Random Forest Machine Learning

Ko,

Park

2024

Sustainability

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South Korea’s Particulate Matter (PM) concentration is among the highest among Organization for Economic Cooperation and Development (OECD) member countries. However, many studies in South Korea primarily focus on housing characteristics and the physical built environment when estimating apartment prices, often neglecting environmental factors. This study investigated factors influencing apartment prices using transaction data for Seoul apartments provided by the Ministry of Land, Infrastructure, and Transport (MOLIT) in 2019. For this purpose, the study compared and analyzed a traditional hedonic price model with a machine learning-based random forest model. The main findings are as follows: First, the evaluation results of the traditional hedonic price model and the machine learning-based random forest model indicated that the random forest model was found to be more suitable for predicting apartment prices. Second, an importance analysis using Explainable Artificial Intelligence (XAI) showed that PM is more important in determining apartment prices than access to education and bus stops, which were considered in this study. Finally, the study found that areas with higher concentrations of PM tend to have higher apartment prices. Therefore, when proposing policies to stabilize apartment prices, it is essential to consider environmental factors. Furthermore, it is necessary to devise measures such as assigning PM labels to apartments during the home purchasing process, enabling buyers to consider PM and obtain relevant information accordingly.

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Appraisal of Apartments in Albania using Hedonic Regression

Nurja

Jaupi

Elezaj

2022

Wseas Transactions on Business and Economics

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Valuation of property is an important part of the land organization framework since it has a direct impact on people’s lives. The adjustments within the property estimation can dramatically change the abundance of the organizations and their ability for development. A variety of people may benefit from a better property valuation framework in our society because it reduces the risk of investing in this division and encourages lower interest rates on loans. The aim of this paper is to empirically demonstrate that, aside from location and address of properties, there are other major factors that influence the price of flats in Albania by developing a hedonistic pricing model. The capital city of Tirana and also Durres were selected for this research paper in order to develop a hedonic price model based on the data collected by properties sold at one of the largest real estate agencies, “Century 21” in the country. According to the findings, apartment attributes such as area of living, number of bedrooms and other factors influence the price. The findings also revealed the marginal influence of the number of apartment spaces, which was dependent on the living area of the apartment. This is the first empirical study to present the results obtained from a hedonic regression built using data pertaining to Albania.

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Determinant of Housing Rents in Urban Albania: An Empirical Hedonic Price Application With Nsa Survey Data

Cited by 3 publications

References 17 publications

Modelling coastal externalities effects on residential housing values

Modelling coastal externalities effects on residential housing values

Investigating the Correlation between Air Pollution and Housing Prices in Seoul, South Korea: Application of Explainable Artificial Intelligence in Random Forest Machine Learning

Appraisal of Apartments in Albania using Hedonic Regression

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